Quantitative Researcher

As a Quantitative Researcher, you will be engaged in impactful projects aimed at enhancing the specification and implementation of our investment models, as well as conducting research initiatives to improve decision-making in portfolio construction within our fully integrated, unified systematic investment process.
Responsibilities: Your responsibilities will evolve based on your experience and capabilities. Depending on your competitive advantages, you may be involved in the following typical tasks:
Merging, structuring, and analyzing substantial volumes of data from diverse sources.
Evaluating the quality of historical and current data, identifying deficiencies, and proposing corrective measures.
Conducting ad-hoc exploratory statistical analysis across multiple large and complex data sets from various structured and unstructured sources.
Developing and maintaining production-quality code directly utilized in the investment process.
Investigating predictable patterns in asset returns, risks, trading costs, and other data relevant to financial markets.
Conducting portfolio construction research using our simulation capability.
Collaborating with software engineers to design feeds for new data sources from third-party vendors.
Participating in data architecture decision-making to support the Research data platform.
Qualifications:
Graduated from an undergraduate or graduate program in finance, mathematics, economics, or a closely-related discipline with a focus on quantitative and financial analysis.
Demonstrated professional or academic success, with recent graduates encouraged to apply.
Strong analytical, quantitative, and problem-solving skills.
Understanding of probability, statistics, linear regression, time-series analysis, linear algebra, calculus, optimization, and portfolio theory.
Knowledge of the application of statistics to economics, including econometrics or regression analysis.
Experience with a statistical computing environment such as Python, Stata, R, or MATLAB.
Experience analyzing large data sets.
Understanding of finance, including equities and derivatives.
Passion for financial markets.
Excellent communication skills, including proficiency in data visualization.
High energy and a strong work ethic.
Additional Considerations:
Good understanding of the academic field of empirical asset pricing.
Familiarity with financial data products.
Experience with stock market data.

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